title: Respiration amplitude analysis for REM and NREM sleep classification.
abstract: In previous work, single-night polysomnography recordings (PSG) of respiratory effort and electrocardiogram (ECG) signals combined with actigraphy were used to classify sleep and wake states. In this study, we aim at classifying rapid-eye-movement (REM) and non-REM (NREM) sleep states. Besides the existing features used for sleep and wake classification, we propose a set of new features based on respiration amplitude. This choice is motivated by the observation that the breathing pattern has a more regular amplitude during NREM sleep than during REM sleep. Experiments were conducted with a data set of 14 healthy subjects using a linear discriminant (LD) classifier. Leave-one-subject-out cross-validations show that adding the new features into the existing feature set results in an increase in Cohen's Kappa coefficient to a value of κ = 0.59 (overall accuracy of 87.6%) compared to that obtained without using these features (κ of 0.54 and overall accuracy of 86.4%). In addition, we compared the results to those reported in some other studies with different features and signal modalities.
authors.forename: Xi
authors.initials: X
authors.lastname: Long
authors.forename: Jérôme
authors.initials: J
authors.lastname: Foussier
authors.forename: Pedro
authors.initials: P
authors.lastname: Fonseca
authors.forename: Reinder
authors.initials: R
authors.lastname: Haakma
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Aarts
mesh_terms: D056044:Actigraphy; D000328:Adult; D000465:Algorithms; D016002:Discriminant Analysis; D004562:Electrocardiography; D001330:Electronic Data Processing; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D016014:Linear Models; D008297:Male; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology*; D055815:Young Adult